{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example showing the plot_learning_curve method used by a scikit-learn classifier\n",
    "\n",
    "In this example, we'll be plotting the learning curve of a `RandomForestClassifier` to see both training and cross-validation scores. We'll call the `scikitplot.estimators.plot_learning_curve` method, and supply our classifier and our\n",
    "features and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_breast_cancer as load_data\n",
    "\n",
    "# Import scikit-plot\n",
    "import scikitplot as skplt\n",
    "\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (14, 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "X, y = load_data(return_X_y=True)\n",
    "\n",
    "# Create classifier instance\n",
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lIonVPs1AEIiANjeWHdPw1LBi4RihB0BZwS0ok89oIDug4qHikntp/OWLXYBT\nKn3Y0h3rLgeY7ni3Tt146rzgMqeXJjE30LT61M6X7b6MAFRH807oayxbbmBYbNvZb3b276ade9/z\nPE3mJ8uh4lgho9Y2/v1QaPb3PaTS7coPLxf7bmRkzMyXlviYinMB/zH+soXuB/khw2oQiIA2Za3V\n0exRHZ46rM54659YAJir4BYWDTHl27mKHpqKXpo5M6A9MH//IROa1/ty+sbTy8FlwbKzmdsdsQ7e\nd9bYanoUaoWMpeyrsgdhZuWS7i/UC7HoPjQbGpYaGJYTEKq/V+5/oYAwEBnQjg075q1f7DEL3Ufj\nEIiANmSt1ej0qI5kj6gr3sWbLdCE8k5+8RBTcT+Tz8zbLufkFt1/2IRnA8pMgDmh44R54WVicEK7\nz949r5emI9rBe8cqedZTwS3MnWJ7gR6EefdntvHsTA9ExWMW3IeOHRj8gOD3KFSuW+r3ypCx1JP9\n8nNawWOa+ffQyLRsGVk7IRABbcZaq5GpEY3nxtUVIwwBjZJzcsecsnmhQDORm1DOXTzQREKReb0u\nWzq31Cwvq/7qifcsuTx2r7NXvaczrfNqWWtV9IpyPEee9WStLZcpp6IpRUKlU7LFTvZrBYKB8IBO\n23Takh8DYD4CEdBGPOtpeHJYE/kJdcW7gm4OsGp37r+zIYPSrbVzA80CvTTpfLp0rZrc/DE1eTe/\n6DGioejcQJPo1taurfNCTGXA6Y7NznaWjCQ5yW1ijueo6BblWlfWWoVMSMlIUj3JHsUj8fK4zdUy\nMnXZD9DO+B8EtAnPehrKDGm6OE0Ywrpw5/4750xbPJAZ0Id//GFJ0mvPfq1yTq4cUpY7ZfNEfkIF\nt7Do8aOhaHkaZj+0VF6HpnIcTa0emkQkQaBZJ1zPLfX+uE55tq14OK7ueLdS0ZSi4aiioSivN9Ck\nCERAG3A9V4OZQeWcnDpiHUE3B1i10elR/f1dfz934L+krJPVB374AV31o6tU9IqL7iMWjs0bL7O9\nZ/uC0zT7PTP+xTgJNO3JL30rurMXo46YmdK3ZEqxSEyxcIwJI4AWQiAC1jnHczQwMSDHcwhDaDmu\n5+qp8ae09/Be7RvZp32H92nv4b0anhpe8DGe9fSe894zb7az6l4aBjpjKYpuadyP4zkyxsyWvs30\n8kXDUUrWgBbH/2BgHSu6RfVN9Mlaq2Q0GXRzgEVNFaa0b3Qm9Izs1b7D+7R/dH95trRIKKJdm3bp\neTufpz1Az0SlAAAgAElEQVSb9+jT939ao9Oj8/azrWub/uZ5f7PWzcc64HquCm5BrueWp32Oh+Pa\nkNigRCRRHvdDzyCwvhCIgHWq4BbUn+6XFWEIzcVaq8HJwXLwufd396rvN306MH6gfP2SDfEN2r15\nt/7sGX+mPZv3qHdzr3Zt2qV4JF7ez+bU5jljiCQpGUnq2kuuXfPnhNbjWU9Ft1gqrZy5mGY0HFVn\nrFOpaEqxcEzRcJTSN6ANEIiAdSjv5NWX7lM4FKYsCIEquAU9dvSxOb0+ew/v1XhuvLzN1sRWPWv7\ns/T6Pa9X7+Ze9W7u1daurcf8FN6fTa4Rs8xhfbHWlmZ984ryPE9S6Xo8qWhKm6KbFAuXxv2EQ+GA\nWwogCAQiYJ3JOTn1pfsUDUcVC8eCbg7ayFh2rBx4/O+PHXmsPLlBIpzQ2cefrVfsekW51+fs48/W\nwYcPqvf8lV3n5rLdlxGAME/llNeykkyp93BTclNp3E8oqmg4GnQzATQJAhGwjkwXp9WX7isP9AUa\nwbOeDowfmBd+BjOD5W1O6DhBe47foxec8oJy+Dl146kMPkfdedYrj/vxrCcjo2g4qq5412zpG1Ne\nA1gEf5mAdWIyP6mBzICS0SQnnaibbDGrR0YfmRN89h/er6nilCQpZEI6Y9MZunDbheXgs2fzHm3u\n2Bxwy7Ee+VNeO54jz3qy1ioSmpnyOppSPBJXNBSl9A3AsnDWBKwDE7kJDWYG1RHr4EQAK2Kt1cjU\nyLxenyfHnpRnS2MuOmOd2rN5j/6k90/K4efM485k0g40TGXpm7V2dsrrZI/ikXh51jcAWA3eRYAW\nl86lNZgZVGeskzCEJXE8R08cfaIcevwAVDmF9cndJ6t3c69edearyr0+23u2M+MWGsb13FLvj+tI\nRpItXTy3O96tVDSlaDhK6RuAhiAQAS3saPaoRiZH1BXv4kQVNU3kJ7T/8P45vT6Pjj6qvJuXVDrh\nPPO4M/WiU19U7vXZvXm3NiQ2BNxyrGd+6VvRLZbG/RijiJkpfUumFIuUZn3jfQ3AWiAQAS3IWqsj\n00c0mh1VV7yLT0wha636J/pne31G9mrf6D4dTB8sb7MpuUl7Nu/R2571tnKvzxmbzmACDjRc0S2N\n+3E8R8aY2dK3eE95EhhK3wAEhXcfoMVYa3V4+rCOTh8lDLWpnJPTY0cem+31mQk/E/kJSZKR0akb\nT9UzT3ym3vi0N5bDz5bOLfy+oOFczy3P+mZlZYxRPBzXhsQGJSKJ8rgffhcBNAsCEdBCrLUanhpW\nOpcmDLWJI9NH5pS77RvZp8eOPla6vopK11bZvXm3XnPWa+aUvKWiqYBbjnbgWa/c+2OtlZVVNBxV\nZ6xzdsrrcJTSNwBNjUAEtAjPejqUOaTJ4qS64l1BNwd15nqunhp/qhx+9o3s077D+3Ro6lB5my2d\nW9S7uVcvPv3F6j2h1OtzSs8pTKaBNWGtLc365hXleZ6srMKhsFLRlDZFNykWLo374fcRQKshEAEt\nwPVcDWYGlS1m1RnvDLo5WKWpwpT2j+6fU/L2yOgjyjpZSVIkFNGuTbv03B3PLfX6nNCr3s292pTc\nFHDL0U78Ka89z9NkflIyUiKS0MbERiWjSUVDUcafAVgXCERAk3M9V/0T/Sq6RcJQi7HWamhyaO5Y\nn8P79Pvx38vKSpJ64j3as3mP3vT0N5WDz65NuxSPxANuPdpJZembZz0ZGUXDUXXFuxQNR7Vzw07F\nwjHKdAGsSwQioIk5nqP+dL9c6yoVY0xIMyu4BT1+9PF54WcsN1beZmfPTvVu7tXrdr+uXPK2rWsb\nJ5lYU/6U147nyPVcGZly6VsqmlI8Elc0FC2XvoVMiIAOYF0jEAFNqugW1TfRJ2utktFk0M1BhbHs\nWGmcz+hs8Pndkd+p6BUlSYlwQmcdf5ZedsbLysFn9/G7GfuFQPilb651Za1VyISUiCTUk+xRPBIv\nz/oGAO2Kd0CgCRXcgvrSfTLGEIYC5FlPB9MHy6Fn7+G92nt4rwYzg+VtNqc2q3dzr56/8/nl8HPa\nxtM4wUQgPOup4BbkuI5kJNnSxXe7491KRpOlWd9CUXolAaACf7GBJpNzcupL9ykSilCmsoayxawe\nPfKo9o7snZ3p7fA+TRWnJJXKhk7feLou2HpBeaKDPZv36ISOEwJuOdqVX/pWdIvlMWkREymVviVT\nikVKs74x5TUALI5ABDSRbDGrvnRf+UQGjTEyNTKn12ff4X16YuwJedaTJHXGOrVn8x798Z4/Lgef\ns447i946BMqf9MDxHBljZGSUiqbUE+9RIpJQNBylZxIAVoB3TqBJTBen1ZfuK5/YYPUcz9ETR5/Q\nT0d+qm/97FvlADQ6PVreZlvXNvWe0KtX7HpFeZa37T3b+VQdgXI9VwW3INdzZWVljFE8HFdPvKdc\n+hYJRSh9A4A6IBABTSCTz2ggM6BUNMUnvCs0kZ/Q/sP75/T6PDr6qHJuTpIUDUV11vFn6YWnvlC9\nm0u9Pns279GGxIaAW452VznltbVWVlbRcFSdsU6loqnSuJ9wlJAOAA3CmRcQsInchAYzg+qIdXCF\n9yWw1mogMzBvooOD6YPlbTYmNqr3hF79+bP+XL2bexUfieslz3sJZYgInLW2NOubV7rgqZUtT3m9\nKbpJsXCpXJb3AgBYOwQiIEBj2TENTw2rM97Jp7815J28fnfkd3N6ffYd3qd0Pi1JMjI6ZcMpesaJ\nz9AbnvYG9W4ulbxt6dwyp5Ro79RewhAC4U957XiOjIxkpEQkoY2JjUpGk4qGopTIAkDACERAQI5m\nj2pkcoQwNOPI9JHZi5oe3qt9I/v0+NjjcjxHkpSMJLV782696qxXlUvedh+/Wx2xjoBbDpT4pW9F\nryhrrYyMouGouuJdSkVTioaiioVjjPsBgCZDIALWmLVWo9OjOpI9oq54V9udHLmeq6fGn5rb6zOy\nT4emDpW32dKxRXtO2KMXn/7i8hTXp/ScQhkRmoY/5bXjOXI9V0ZmTulbPBJXNBTldxYAWgCBCFhD\n1lqNTI1oLDvWFmFoqjCl/aOzEx3sHdmrR0YfUdbJSpLCJqwzjztTz9nxnHKvT+/mXh2XOi7glgNz\n+aVvrnVlrVXIhJSIJNST7FE8Ei/P+gYAaD28ewNrxLOehieHNZGfUHeiO+jm1JW1VocmD5UnONh3\neJ/2juzV78d/X75gZHe8W72be/Wmp7+pHH52HbdLiUgi4NYDc3nWU8EtyHEdyUiyUiwcU3e8uzzl\ndTQUXfcfaABAuyAQAWvAs56GMkOaKk6pK94VdHNWpegW9djRx+aUvO0d2aux3Fh5mx09O9S7uVev\n2/26csnbtq5tnECi6filb0W3KCsra62ioahS0ZRSyVT5IsmM8wOA9YtABDSY67kazAwq5+TUGesM\nujlz3Ln/Tl3/8+s1mBnU1q6tuvaSa3XZ7svK68dz4/OCz2NHH1PBLUiS4uG4zj7+bL30jJfOTnSw\nebe64+urBwzrhz/jm+M5MsbIyCgVTakn3lO+KDKlbwDQXnjXBxrI8RwNTAyo6Babbja0O/ffqQ//\n+MPl8TwDmQFd/e9X6/u/+748edo7slcDmYHy9senjlfv5l49f+fzy70+p208jZNHNC3Xc1VwC3I9\nV1ZWxhjFw3H1xHvKpW+RUISeSwBoc5zJAA1SdIsamBiQa12lYqmgmzPP9T+/vhyGfAW3oB8+8UPt\n2rRL5209T2/d/NZy+Dmh44SAWgocmz/lteM58qwnSYqGo+qMdSoVTZXG/YSjlL4BAOYhEAENUHAL\n6k/3y8oqGU0G3ZyaBjODNZcbGd31trvWtjHAMlVe8FSSwqGwkpGkNiU3KRYujfthymsAwFIQiIA6\nyzt59aX7FAqFlIw0ZxiSpK1dW+eUxFUuB5pJ5cQHfu+PP+ub3/tD6RsAYKUIREAd5Zyc+tJ9ioZL\nV6RvZtc85xpd9aOrytNiS1IyktS1l1wbYKuAqmmvJRljlIwk1ZMqTXxA7w8AoJ4IRECdWGt1cPxg\n6Qr14WjQzTmmvJuXldVxyeN0NHu05ixzwFqw1ipbzMr1XElSJBRRKppSR6qjXP5G7w8AoFEIREAd\nTBWmVHALSkQTLTHrWt7J65P3flLnnnSuvv2Gb3OyiTVjrVXBLajoFWWtlVHpd29DYsOcmd8AAFgr\n/NUBVimTz2ggM6CQCbXMidzXf/t1DU0O6aaX3EQYQkOVp762rqy1CpmQUtGUNiU3KR6JKxaOaTA8\nqONSxwXdVABAm2qNszegSaVzaQ1lhkrXGGqRXJEtZnXLL2/RRdsu0vN2PC/o5mAdsdaWZn/zZic/\niIQi6op3zU59HYoSwgEATaWhgcgY81JJn5QUlvRP1trrq9ZvlHS7pNMl5SS9w1r720a2CaiXseyY\nDk0eUle8q6WubfLV33xVh6YO6VMv/xQnplgVf/ID/8KnkpSIJLQpuak8+UGr9JoCANpXw/5SGWPC\nkm6V9GJJ/ZLuN8Z8x1q7r2Kzj0h6yFr7WmPM2TPbv6hRbQLqwVqrI9NHdHj6sLrj3S0VKrLFrD51\n36f03O3P1cXbLw66OWgxta790xHtUEe0ozyZSCt9OAAAgNTYHqILJD1urX1Skowxd0h6jaTKQLRH\n0vWSZK19xBhzijHmRGvtcAPbBayYtVaj06M6mj3acmFIkr7431/U4enDuu1VtwXdFDS56mv/GBlF\nw9E51/5phdkUAQA4lkYGom2S+iru90u6sGqb/5Z0maT/Z4y5QNJOSSdLmhOIjDGXS7pckk488UTd\nddddDWpyc5ucnGzb594sHM+R67kKheZ/Cp6bymnv/XsDaNXSZN2sbr7vZp274Vx1DHZo72DztrXe\nmv21aRbWWlk7c10qI4UUUigUUkihhoZ/3tuaG69Pc+P1aW68Pq0h6OLu6yV90hjzkKTfSHpQklu9\nkbX2Nkm3SdJ5551nL7300rVsY9O466671K7PPWie9TQ8OayJ/IS64l01t9l7/171nt+7xi1buk/d\n9ymlnbT+9qV/q96tzdvORmj21yYIRbeoolcsX/snGoqqI9ZR7v1Zy2v/8N7W3Hh9mhuvT3Pj9WkN\njQxEA5K2V9w/eWZZmbV2QtLbJcmU/vI+JenJBrYJWDbXczU0OaTpwvSCYajZZfIZfeaBz+iFp75Q\n5249N+jmYI3VuvZPPBLXxsRGrv0DAGh7jfwLeL+kXcaYU1UKQm+Q9KbKDYwxGyRNW2sLkv5S0s9m\nQhLQFFzP1WBmUDknp854Z9DNWbF/fvCfNZ4b1zUXXxN0U7AGKq/9I0khE1Iykpxz7R8mPwAAoKRh\ngcha6xhj3ivpRypNu327tXavMebdM+v/UdJuSV80xlhJeyX9RaPaAyyX4znqT/fLtW7pOkMtKp1L\n67Zf3aY/PP0P9cwtzwy6Oagzrv0DAMDqNLRGwlr7A0k/qFr2jxW375F0ZiPbAKxE0S2qb6JP1lol\no8mgm7Mq//Trf1I6n9bVF18ddFNQB/61fxzXkZWVMUbJSFLHJY8rX/snHAoH3UwAAFoGReNAlYJb\nUF+6r3Si2eJhaCw7ps/9+nN6+a6X62knPC3o5mAFFrz2T5Jr/wAAUA8EIqBC3smrL92ncCiseCQe\ndHNW7bO/+qwmC5P0DrWIymv/+NNfx8Ix9cR7ypMfcO0fAADqi0AEzMgWs+pL9ykWKU053OqOZo/q\nnx/8Z73qrFfp7OPPDro5qKE8+cHM1NfGGKWiKW2IbyhPfkD5GwAAjUUgAiRNF6fVP9FfLkFaDz5z\n/2eULWZ11UVXBd0UzKh17Z/OWKc6Yh1MfgAAQEAIRGh7k/lJDWQGlIwm1821WA5PHdbnH/q8Xnv2\na7XruF1BN6ct+df+cbzS5AfWWiUiCa79AwBAk+GvMdraRG5Cg5lBdcQ61lVp0qcf+LTybl5XXnxl\n0E1pG7Wu/ZOKprQpyrV/AABoZgQitK3x3LgOTR5SZ7xzXZ2oDk8O60sPfUmv2/06nb7x9KCbsy5V\nTn7gX/snFo6pO949O/kB5W8AALQEAhHa0tHsUY1Mjqy7MCRJt95/q4peUR+86INBN2XdWOjaPz2p\nHq79AwBAiyMQoa1Ya3Vk+ohGs6Pqinetu0/wBzOD+srDX9Gf9v6pdm7YGXRzWpZ/7R9/8oNQKDTn\n2j+xcGzd/e4AANCuCERoG9ZajUyNaCw7ti7DkCTdct8t8qyn91/4/qCb0jK49g8AAO2NQIS24FlP\nI1MjGs+Nr9sw1D/Rr6//5ut6w9PeoO0924NuTtPyJz9wPEfS7OQHGxMbS9Ouh6KUvwEA0EYIRFj3\nPOtpKDOkqeKUuuPdQTenYW7+5c0yxuh9F74v6KY0FX/ig0w+I4lr/wAAgLkIRFjXXM/VYGZQ2WJW\nnfHOoJvTMAfGD+gbe7+htzzjLdrWtS3o5gTGs56KbrF87R9JSkQSioQi2t6znWv/AACAeTgzwLrl\neI4GJgZUdIvrOgxJ0id/+UmFTVjvveC9QTdlTZUnPzjGtX+eNE8qFU0F3FoAANCMCERYlxzPUX+6\nX451lIqt7xPhJ8ee1P/d93/19nPeri2dW4JuTsMsdu2fVDRV7v2h/A0AACwHgQjrTsEtqD/dLyvb\nFr0Cn7j3E4qGo7ri/CuCbkpdce0fAACwFghEWFfyTl79E/3lk+f17vGjj+tbj3xL7zr3XTqh44Sg\nm7Mq1df+iYQiSkVT6kiVJj/g2j8AAKARCERYN3JOTn3pPkVCEcUj8aCbsyY+fs/HlYgk9Ffn/VXQ\nTVkWa60KbkFFr3TtHyOjeCTOtX8AAMCaIxBhXcgWs+pL9ykWKfUktINHRh/Rtx/9tq644Aodlzou\n6OYsyr/2j2tdWWtnJz9Izp38AAAAYK0RiNDypovT6kv3KRlNttWUyjfdc5M6Yh1617nvCrop8xTd\noopeafIDay3X/gEAAE2rfc4esS5l8hkNZAaUiqbaKgztPbxX33/s+7rywiu1Kbkp0LYsdO2fTclN\n5ckP2um1AQAArYWzFLSsdC6tocyQOmIdbTfb2E1336TueLfeee471/zY/uQHjudIksKhsDqiHeqI\ndigeiSsajlL+BgAAWgaBCC1pLDum4alhdcY72+7k++Hhh/XDJ36oay6+RhsSGxp6rKVc+4fJDwAA\nQCsjEKHlHJk+osNTh9syDEnSjffcqA3xDfqLZ/9F3fddee0fSVz7BwAArHsEIrQMa61Gp0d1JHtE\nXfGuthyU/+DQg/rJkz/RXz/3r9Ud767bfrPFrFzP5do/AACg7RCI0BKstRqZGtF4blxdsfYMQ1Kp\nd2hTcpPecc476rZPa61cz9UpG09pmynLAQAAfO1Xb4SW41lPhyYPlcJQm/YMSdL9g/frP3//n3rP\nee9RZ6yzbvvNu3l1xjsJQwAAoC3RQ4Sm5llPQ5khTRWn1BXvCro5gbrh7ht0fOp4vfVZb63rfh3X\nUU9HT133CQAA0CroIULTcj1XAxMDmi5O17VHpBXd03ePfn7w57ri/CuUiqbqtl9rZ68bBAAA0I7o\nIUJTcjxHAxMDcjxHHbGOoJsTKGutbrj7Bp3QcYLe8oy31HXffrkcM8cBAIB2RQ8Rmk7RLaov3SfH\nc5SMJoNuTuB+0fcL3Ttwr953wfvq/vMoukX1xCmXAwAA7YseIjSVgltQf7pfVpYwpNneoS2dW/Sm\np7+p7vs2MpTLAQCAtkYPEZpG3snr4PjB0sVACUOSpJ8d+JnuH7xf77/w/XUPLpTLAQAAEIjQJHJO\nTgfTBxUJRxSPxINuTlOw1up/3/2/ta1rm97Q+4a6759yOQAAAAIRmsB0cVoHxg8oFo5xLZwKP33q\np3rw0IO68qIr6x4SKZcDAAAoYQwRAjWZn9RAZkDJaFKREL+OPmutbrjnBu3o2aE/3vPHdd8/5XIA\nAAAl9BAhMBO5CQ1kBpSKpghDVf79iX/Xw8MP68qLrlQ0HK37/imXAwAAKCEQIRDpXLochuilmMuz\nnm645wadsuEUvW736+q+f2utQiZEuRwAAIAomUMAjmaPamRyRF3xLoUMmbzavz32b9p3eJ9ufunN\nDek5y7t5dcQ6CKIAAACihwhryFqr0alRjUwRhhbiWU833nOjTt94uv7o7D9qyDEolwMAAJhFDxHW\nhLVWo9OjOjJ9RF3xLhljgm5SU/ru776rR488qk+//NMN6cHxy+W4zhMAAEAJgQgNZ63V8NSw0rk0\nYWgRrufqpntu0lnHnaVXnvnKhhzDL5ejdw4AAKCEsyI0lGc9DWWGNJGfIAwdw7cf/bYeP/q4rrr4\nqoaN76FcDgAAYC56iNAwrudqaHJI04VpdcY7g25OU3M8Rzfdc5P2bN6jl+96eUOOQbkcAADAfAQi\nNITrueqf6FfRLRKGluCb+7+pp8af0u2vvr1h5WyUywEAAMzHmRHqzvEc9aX75HiOUrFU0M1pekW3\nqE/c+wk9/YSn6w9P/8OGHodyOQAAgLnoIUJdFd2i+ib6ZK2lNGuJ/mXfv+hg+qC++EdfbNgYK8rl\nAAAAaqOHCHVTcAs6mD4oSZx4L1HBLegT935C52w5Ry869UUNO07ezasz1km5HAAAQBXOjlAXeSev\ng+MHFTIhJSKJoJvTMu747R0ayAzomudc09AZ+IpuUd3x7obtHwAAoFVRModVyxaz6kv3KRaJKRaO\nBd2clpFzcrr5lzfrvK3n6fk7n9+w41AuBwAAsDB6iLAq08Vp9U30KR6JE4aW6eu/+bqGJoca3jtE\nuRwAAMDCOEPCimXyGR1MH1QiklA0HA26OS0lW8zqlvtu0cUnX6xLtl/S0GNRLgcAALAwSuawIhO5\nCQ1mBtUR61A4FA66OS3nyw9/WcNTw7r15bc2tHeIcjkAAIDFEYiwbGPZMQ1PDaszThnWSkwXp3Xr\n/bfqkh2X6OLtFzf0WJTLAQAALI6zJCzL0exRDU8Oc5K9Cl986IsanR7VNRdf0/BjUS4HAACwOHqI\nsCTWWo1Oj+pI9oi64l0NLfNazyYLk7r1/lt16c5Ldf628xt6LMrlAAAAjo1AhGOy1mpkakRj2THC\n0Cp9/qHPayw3pqufc3XDj0W5HAAAwLFxpoRFedbToclDGs+NqzvRTRhahUw+o3+8/x/1olNfpGef\n9OyGH49yOQAAgGOjhwgL8qynocyQpopT6op3Bd2clvdPD/6TxvPjuuY5jR87RLkcAADA0hCIUJPr\nuRrMDCrn5NQZ6wy6OS0vnUvrtl/dppec/hI948RnNPx4lMsBAAAsDWdLmMfxHPVP9Cvv5NUR6wi6\nOevC5379OU3kJ9Zk7JBEuRwAAMBS0UOEOYpuUQMTA3Ktq1QsFXRz1oWj2aP63K8/p1fseoV6N/c2\n/HiUywEAACwdgQhlBbeg/nS/rCwn03X02V99VlOFKV118VVrcjzK5QAAAJaOMyZIkvJOXn3pPsmI\nMFRHR6aP6PYHb9erz3q1zj7+7DU5JuVyAAAAS0cPEZRzcupL9ykajioWjgXdnHXlMw98Rjknt2a9\nQ5TLAQAALA+BqM1li1n1pfsUj8QVDUeDbs66cnjqsD7/0Of1R2f/kc7YdMaaHDPv5tUV66JcDgAA\nYIkIRG1sqjCl/ol+JaNJRUL8KtTbrfffqqJb1Acv+uCaHbPoFtXVwTWjAAAAloqz4DaVyWc0kBlQ\nR7RD4VA46OasO4cmD+nL//1lvW7P63TaxtPW5JiUywEAACwfgagNpXNpDWWG1BEjDDXKrffdKsc6\nuvLCK9fsmJTLAQAALB+BqM2MZcc0PDWszjjTMjfKQGZAX/nNV/SnvX+qnRt2rtlxKZcDAABYPgJR\nm7DW6mj2qA5PHVZXvEvGmKCbtG7d8stbZK3V+y98/5odk3I5AACAlSEQtQFrrUanR3Uke4Qw1GB9\n6T7d8ds79Manv1End5+8ZselXA4AAGBlCETrnLVWI1MjGs+NqytGGGq0m395s4wxet8F71vT41Iu\nBwAAsDIEonXMs56GJ4c1kZ9QV5yT5UYbzA7qG3u/obc+863a2rV1zY5LuRwAAMDKEYjWKc96GsoM\nabo4TRhaI1/r+5qioaiuuOCKNT0u5XIAAAArxxnUOuR6rgYmBjRdnFZHrCPo5rSFJ8ee1E+Gf6K3\nPPMt2tK5ZU2PXXSL6k50r+kxAQAA1gt6iNYZx3PUn+6Xa13C0Br6+L0fL/UOnb+2vUPWWhljlIgk\n1vS4AAAA6wU9ROtI0S2qL90n17qMJ1lDjx15TP/6yL/q1Vtfrc0dm9f02Hk3r+5YN+VyAAAAK8RZ\n1DpRcAvqS/fJyhKG1thN996kZCSpPzn5T9b82JTLAQAArA6BaB3IO3kdHD9I6VQA9h/er+8++l29\n45x3qCfas6bH9meX4zUHAABYOQJRi8s5OR1MH1QkHFE8Eg+6OW3npntvUkesQ+86911rfmxmlwMA\nAFg9zqRa2HRxWgfTBxULxxQLx4JuTtv57chv9YPHfqB3Pvud2pjcuObHp1wOAABg9ZhlrkVN5ic1\nkBlQMppUJMTLGISb7rlJ3fFuvfPZ71zzY1MuBwAAUB/0ELWgidyE+if6lYqmCEMBeXj4Yf3oiR/p\n8nMvV09ibccOSaVSScrlAAAAVo+z6RaTzqU1NDmkzngnJ8MBuuHuG7QhvkF/ec5fBnJ8x3MolwMA\nAKgDzqhbiGtdDWWG1BkjDAXp10O/1n889R969/nvVle8a82Pz8VYAQAA6oceohZgrdWR6SMqukV1\nxbtkjAm6SW3thrtv0KbkJr3jWe8I5Pg5J8fFWAEAAOqEM6omZ63V4enDGp0eVTgUJgwF7L6B+/Rf\nB/5LV5x/hTpiHYG0gXI5AACA+qGHqIlZazU8Nax0Lh1IaRbmu+HuG7Q5tVlvfeZbAzk+5XIAAAD1\nRQ9Rk/Ksp6HMkCbyE5TJNYm7++7WL/p+oSsuuELJaDKQNlAuBwAAUF/0EDUh13M1mBlUtphVZ7wz\n6J2D3DIAACAASURBVOZApZ6ZG+++USd2nKg/e/qfBdYO17qUywEAANQRHzM3Gddz1T/Rr7yTJww1\nkZ/3/Vz3Dtyr913wvsB6h6y1kkS5HAAAQB3RQ9REHM9Rf7pfjnWUiqWCbg5mWGt1w9036KTOk/TG\np78xsHZQLgcAAFB/nFk1iaJb1MHxg3Ktq1SUMNRM/uvAf+mBwQf0/gvfH2jvDOVyAAAA9UcPURMo\nuAX1pftkjFEyEkw5Fmrze4dO7j5Zb3jaGwJth0S5HAAAQL3RQxSwnJPTgfEDCpkQJ7tN6CdP/UQP\nHnpQV154pWLhWGDtoFwOAACgMeghClC2mFVfuk+xSCzQk23U5vcO7ezZqdfveX2gbaFcDgAAoDH4\nuDkg08VpHUwfVDwSJww1qR898SP9duS3uvKiKxUNRwNrB+VyAAAAjUMPUQAy+YwGMgNKRVOKhHgJ\nmpFnPd1w9w06dcOpumz3ZYG2hXI5AACAxuEMa41N5CY0MDGgjmgHYaiJ/eCxH2j/6H5ddfFVgb9O\nlMsBAAA0Dmfka2gsO6bhqWF1xjv5tL+JuZ6rG++5UWdsOkOvOes1gbaFcjkAAIDG4qx8jRzNHtXw\n5LA6Y4ShZve9331PvzvyO1118VUKh8KBtoVyOQAAgMaih6jBrLUanR7VkewRdcW7ZIwJuklYhN87\ndNZxZ+lVZ74q6ObI8RzK5QAAABqIQNRA1lqNTI1oLDtGGGoR//rIv+qJsSd02ytvC7xXxlorYwzl\ncgAAAA1EIGoQz3oanhzWRH6CT/hbhOM5uunem9S7uVcv2/WyoJtDuRwAAMAaIBA1gGc9DWWGNFWc\nUle8K+jmYIm+ue+b+v347/X513y+KUII5XIAAACNRyCqM9dzNZgZVM7JqTPWGXRzsERFt6iP3/tx\nPePEZ+jFp7046OZQLgcAALBGgv8YfB1xPEf9E/3KO3l1xDqCbg6W4f/s/T/qm+jTNRdf0xRjvSiX\nAwAAWBv0ENVJ0S2qf6JfnvWUiqWCbg6WIe/k9clfflLnbDlHLzz1hUE3RxLlcgAAAGuFj5/roOAW\n1Jfuk2c9JaPJoJuDZbpj7x0ayAzoQ8/5UFP0DlEuBwAAsHboIVqlvJNXX7pP4VCYE9gWlHNyuvmX\nN+v8refrf+z8H0E3RxLlcgAAAGuJM65VcDxHfek+RcIRxSPxoJuDFfjab76mQ5OHdM1zmmPskES5\nHAAAwFoiEK2C4zlyratYOBZ0U7AC2WJWt9x3iy4++WI9d/tzg26OpFK5XMiE6G0EAABYI5TMoW19\n6eEvaWRqRJ95xWeapnco5+TUFeuiXA4AAGCNcNaFtjRdnNat992q5+14ni46+aKgm1NGuRwAAMDa\namggMsa81BjzqDHmcWPMtTXW9xhjvmuM+W9jzF5jzNsb2R7A94WHvqAj2SO6+jlXB92UMsrlAAAA\n1l7DApExJizpVkkvk7RH0huNMXuqNrtC0j5r7TMlXSrpRmMMA3LQUJOFSX36/k/rBae8QOdvPT/o\n5pRRLgcAALD2GnnmdYGkx621T1prC5LukPSaqm2spC5TGsDRKemoJKeBbQJ0+4O3ayw3pqsvbp7e\nIYlyOQAAgCA0MhBtk9RXcb9/ZlmlT0naLWlQ0m8kfcBa6zWwTWhzE/kJffaBz+oPTvsDnXPSOUE3\np4xyOQAAgGAEPcvcSyQ9JOmFkk6X9GNjzP+z1k5UbmSMuVzS5ZJ04on/P3t3Hh5Vefd//HNnTyAk\ngArKIrgTAkQIm4gGrdZdEH8VRa0rLmClVB9p7eWv5XfZx1ZAEFFEK248oNXi0qLWLYqyo1FkE0TZ\nRRZZAmSZmfv3ByEPSwIB5sx9zsz7dV1czZyczHzDzfjM57k/c6aJiouLYz1njaysKkIVSkqKTcWp\nbEeZFsxZEJPHilcvr3hZW8q3qE9On6j/XR7N+kRsRMkmWWuS1kR1JuxWWlrqm/9u4ECsj7+xPv7G\n+vgb6xMMXgaiNZJa7HW7edWxvd0s6RFrrZW0zBjzvaQzJM3e+yRr7XhJ4yWpsLDQFhUVeTXzYSkL\nlWnFlhXKTs+OyeMtmLNAbTu3jcljxaMtZVv0xuw3dNHJF6l3r95Rv/+jWZ/t5dvVIqeFslKzojwV\nJKm4uFh++e8GDsT6+Bvr42+sj7+xPsHg5dbGHEmnGmNaV10ooZ+kt/Y7Z6Wk8yXJGNNE0umSlns4\nExLY+Hnjta18m6+uLCdRlwMAAHDJsx0ia23IGDNI0nuSkiU9Z61dYIy5s+r74yT9P0nPG2PmSzKS\nHrDWbvRqJiSuzbs269kvntVlp12mvGP3v9ihW2WhMjVIb8DV5QAAABzw9D1E1tqpkqbud2zcXl+v\nlXShlzMAkvT03Ke1s3KnhnQb4nqUA4QioZjVLgEAALAv/l/SiHsbd27UcyXP6crTr9Tpx5zuepx9\nUJcDAABwi0CEuPfUnKdUFirTb7v/1vUoB6AuBwAA4BavwhDXftrxk57/6nn1OaOPTml0iutxDkBd\nDgAAwC0CEeLa2DljVRmu1OBug12PcgDqcgAAAO4RiBC3fiz9US999ZKuzrtaJzU8yfU4B6AuBwAA\n4B6vxBC3xswao7AN+3J3SKIuBwAA4AcEIsSlNdvW6H+++R9d0/Yatcxp6XqcA1CXAwAA8AcCEeLS\n47Mfl7VW93a91/UoNSoLlSknI4e6HAAAgGO8GkPcWbV1lSZ/M1nXtbtOzRo0cz1OjUKRkOqn1Xc9\nBgAAQMIjECHujJ41WskmWYO6DHI9So2oywEAAPgHgQhx5YctP+jVBa/q+vbX64TsE1yPUyPqcgAA\nAP7BKzLElVEzRyk1KVUDOw90PUqtqMsBAAD4B4EIceO7n7/T64te140FN6pJ/Saux6kRdTkAAAB/\nIRAhboyaMUrpyem6u/Bu16PUirocAACAv/CqDHHh203fasriKbq54GYdW+9Y1+PUKhQJKTuND2MF\nAADwCwIR4sLIGSOVlZqluzrf5XqUWu2py6WnpLseBQAAAFUIRAi8RRsW6e1v39atHW9Vo8xGrsep\nFXU5AAAA/+GVGQJv5IyRyk7L1oCOA1yPclDU5QAAAPyHQIRA++anbzR12VTd3vF2Ncxs6HqcWkVs\nhLocAACADxGIEGgjZoxQTnqObut4m+tRDqo8VE5dDgAAwId4dYbA+urHr/Sf7/6jAZ0GKCcjx/U4\nB0VdDgAAwJ8IRAis4TOGKzcjV7eeeavrUQ6KuhwAAIB/EYgQSPPWztNH33+kuwrvUna6v3deqMsB\nAAD4F6/QEEjDZwxX48zGurngZtejHBJ1OQAAAP8iECFwZq2epU9XfKq7O9+temn1XI9zUNTlAAAA\n/I1AhMAZPmO4js06Vr/u8GvXoxwSdTkAAAB/41UaAuXzlZ9r+qrpGtRlkDJTM12Pc0jU5QAAAPyN\nQITAsNZqxIwRalqvqfq36+96nEPaU5fLSMlwPQoAAABqQSBCYExbOU2z1szSPV3vCcTu0J66nDHG\n9SgAAACoBYEIgWCt1fDpw3V8/eN1bf61rsepE+pyAAAA/kcgQiAU/1Cseevm6d5u9wbiim3U5QAA\nAIKBQATfs9bq0emPqnmD5rqm7TWux6kT6nIAAADBQCCC772//H19tf4rDe46WGnJaa7HqRPqcgAA\nAMFAIIKv7XnvUKucVro672rX49QJdTkAAIDgIBDB195d9q4WbFigwd0HKzU51fU4dUJdDgAAIDgI\nRPCtiI1oxIwROqnhSepzRh/X49QZdTkAAIDgIBDBt/699N9atHGRhnQbopSkFNfj1Al1OQAAgGAh\nEMGXwpGwRs4YqVMbnaorTr/C9Th1Rl0OAAAgWAhE8KW3v31b3276VkO6D1FyUrLrceqMuhwAAECw\nEIjgO6FISCNmjNAZjc/QZadd5nqcw0JdDgAAIFiC8cYMJJQpi6do+c/L9czlzyjJBCezR2yEuhwA\nAEDABOfVJhJCZbhSo2aMUv5x+br4lItdj3N4rKjLAQAABAw7RPCV1xe9rh+2/qAJV04I1E5LxEYk\nI+pyAAAAAcMOEXyjIlyhUTNHqUOTDrrgpAtcj3NYykPlSjbJgQpxAAAAIBDBR15d8KpWbVul+866\nL3DBIhwJK9kE52p4AAAA2I1ABF8oD5Vr9KzR6nh8R/Vq1cv1OIclYiMyxgQuxAEAAIBABJ+Y9M0k\nrd2+VvefdX/ggsWeD2MFAABA8BCI4FxZqExjZo1Rl2Zd1LNlT9fjHLZwJMzV5QAAAAKKQATnXv76\nZf2440fd1z147x3aU5fj6nIAAADBRCCCU7sqd+mJ2U+oe/Pu6tGyh+txDtueulzQghwAAAB243OI\n4NQLX72gDTs36OnLnnY9yhEJRULU5QAAAAKMHSI4s6Nih56c86TOOfEcdW3e1fU4hy1iI0oySdTl\nAAAAAoxABGeeL3lem3Zt0u+6/871KEeEuhwAAEDwEYjgxPby7Xpy7pM6r9V5Kjyh0PU4R4S6HAAA\nQPARiODEcyXPaUvZFv3urGDuDlGXAwAAiA8EIsTctvJtenru07rgpAtU0LTA9ThHhLocAABAfCAQ\nIeae/eJZbS3fqvvOus/1KEeMuhwAAEB8IBAhpn7e9bPGzxuvi0+5WPnH5bse54hQlwMAAIgfBCLE\n1Pgvxmt7xXYN6T7E9ShHjLocAABA/CAQIWY279qsv3/xd11+2uXKOzbP9ThHjLocAABA/CAQIWbG\nzR2nnZU7A707FLERJSclU5cDAACIEwQixMTGnRv13JfPqfcZvXVa49Ncj3PEykPlapDegLocAABA\nnCAQISaenPOkysPlGtxtsOtRjgp1OQAAgPhCIILn1peu1wslL+iqNlfplEanuB7niFGXAwAAiD8E\nInhu7JyxqoxUanDXYO8OUZcDAACIPwQieGrd9nV6+euX9X/y/o9aN2ztepyjQl0OAAAg/hCI4Kkx\ns8cobMO6t9u9rkc5KtTlAAAA4hOBCJ5Zs22N/mf+/+iatteoZU5L1+McFepyAAAA8YlABM+MnjVa\nxhjd2zXYu0MSdTkAAIB4RSCCJ1ZuXalXFryi6/KvU7MGzVyPc1SoywEAAMQvAhE8MXrmaCWbZA3q\nMsj1KEetPFSunPQc6nIAAABxiECEqPv+5+/1j4X/0PXtr9fx2ce7HueohSIh1U+r73oMAAAAeIBA\nhKgbNWuUUpNT42J3iLocAABAfCMQIaqWbV6mfy76p37d4dc6rt5xrsc5atTlAAAA4huBCFH12IzH\nlJ6crrs73+16lKigLgcAABDfCESImiUbl+jNJW/qljNv0TFZx7ge56hRlwMAAIh/BCJEzciZI5WV\nmqU7C+90PUpUUJcDAACIfwQiRMXCDQv1r2//pds63qZGmY1cjxMV1OUAAADiX50DkTHmbGPMzVVf\nH2uMae3dWAiakTNGKjstWwM6DXA9SlRQlwMAAEgMdQpExpj/K+kBSb+vOpQq6WWvhkKwzF8/X+8s\ne0cDOg1Qbkau63GiojxUrtz0XOpyAAAAca6uO0R9JF0haYckWWvXSsr2aigEy4gZI5STnqPbOt7m\nepSoCUVCqpdWz/UYAAAA8FhdA1GFtdZKspJkjOGVIiRJJT+W6P3l7+uOwjvUIL2B63GigrocAABA\n4qhrIHrVGPO0pFxjzO2SPpD0jHdjIShGTB+h3Ixc3Xrmra5HiRrqcgAAAIkjpS4nWWuHG2MukLRN\n0umSHrLWvu/pZPC9uWvn6qMfPtIfzv5DXF2NjbocAABA4jhkIDLGJEv6wFrbSxIhCNWGTx+uxpmN\ndVPBTa5HiRrqcgAAAInlkJU5a21YUsQYkxODeRAQM1fP1LSV0zSwy8C42k2hLgcAAJBY6lSZk1Qq\nab4x5n1VXWlOkqy1v/FkKvje8OnDdVy943Rj+xtdjxJV1OUAAAASS10D0T+r/gD6fOXnmrF6hoYV\nDVNmaqbrcaKGuhwAAEDiqetFFV4wxqRJOq3q0BJrbaV3Y8GvrLUaPmO4mtZrqv7t+7seJ6qoywEA\nACSeOl122xhTJGmppLGSnpT0rTHmHA/ngk9NWzlNs9fM1j1d74m7nZRQJKT66fFztTwAAAAcWl0r\ncyMkXWitXSJJxpjTJE2S1MmrweA/1lo9Ov1RnZB9gq7Nv9b1OFG1py6XnpzuehQAAADEUF0/mDV1\nTxiSJGvtt5JSvRkJfvXR9x/pi3Vf6N6u9yo9Jb6CA3U5AACAxFTXHaK5xphnJb1cdbu/pLnejAQ/\n2vPeoRYNWuhXbX/lepyooy4HAACQmOoaiO6SNFDSnstsT9Pu9xIhQby//H19vf5rjbhwhNKS01yP\nE1URG1FKUgp1OQAAgARU10CUImm0tXakJBljkiXx6jFBRGxEj05/VK1yWunqvKtdjxN15aFy5aTn\nUJcDAABIQHV9D9GHkvb+wJlMSR9Efxz40bvL3tXCDQv12+6/VUpSXTN0cFCXAwAASFx1DUQZ1trS\nPTeqvs7yZiT4ScRGNGL6CJ3c8GT1PqO363GijrocAABAYqtrINphjOm454YxplDSLm9Ggp/869t/\nafGmxRrSfUhc7g6VhcqoywEAACSwur7CHSzpH8aYtVW3j5d0jTcjwS/CkbBGzhip0xqfpstPu9z1\nOJ4IR8LU5QAAABLYQXeIjDGdjTFNrbVzJJ0h6RVJlZLelfR9DOaDQ28teUtLNy/VkO5DlJyU7Hqc\nqKMuBwAAgENV5p6WVFH1dXdJf5A0VtLPksZ7OBccC0VCGjFjhNoc00aXnnqp63E8QV0OAAAAh6rM\nJVtrN1d9fY2k8dba1yW9bowp8XY0uPTPRf/U91u+17OXP6skU9e3mgULdTkAAAAc6pVusjFmT2g6\nX9JHe30v/t5hD0lSZbhSo2aOUv5x+brolItcj+MJ6nIAAACQDh1qJkn6xBizUbuvKjdNkowxp0ja\n6vFscOS1ha9pxdYVer7383FbJysLlSk3PTdufz8AAADUzUEDkbX2YWPMh9p9Vbn/WGtt1beSJN3j\n9XCIvYpwhUbNGqWCJgX6RetfuB7HM9TlAAAAINWh9matnVnDsW+9GQeuvbLgFa3etlqPnP9I3O6e\nUJcDAADAHvH5bnkckfJQuUbPHK1Ox3dSUasi1+N4hqvLAQAAYA8CEapN+maS1pWu031n3RfXYSFi\nI9TlAAAAIIlAhCq7KndpzKwx6tqsq3q27Ol6HM9EbETJJpm6HAAAACQRiFDl5fkv68cdP8b97hB1\nOQAAAOyNQATtrNypJ2Y/obNanKWzWpzlehxPUZcDAADA3vhwVejFr17Uxp0b9czlz7gexVPU5QAA\nALA/dogS3I6KHRo7Z6zOPfFcdWnWxfU4nqIuBwAAgP0RiBLchJIJ2rxrs37X/XeuR/EcdTkAAADs\nj0CUwLaXb9dTc5/Sea3PU6cTOrkex1PU5QAAAFATAlEC+/uXf9eWsi26r/t9rkfxHHU5AAAA1MTT\nQGSMucgYs8QYs8wYM7SG799vjCmp+vONMSZsjGnk5UzYbWvZVo2fN14XnnyhOjTt4Hocz1GXAwAA\nQE08C0TGmGRJYyVdLClP0rXGmLy9z7HWPmqtLbDWFkj6vaRPrLWbvZoJ/+uZL57R1vKtCfPeIepy\nAAAAqImXO0RdJC2z1i631lZImizpyoOcf62kSR7Ogyo/7/pZz3zxjC455RLlH5fvehzPUZcDAABA\nbYy11ps7NuZqSRdZa2+run2DpK7W2kE1nJslabWkU2raITLGDJA0QJKaNGnSafLkyZ7MfLisrCpC\nFUpKis1bscp2lCmjXsZR38+EHyZo8qrJGtdxnFrXax2FyfwtYiNKS06TkbeBqLS0VPXrU8vzI9bG\n31gff2N9/I318TfWx51evXrNs9YW1uVcv3ww6+WSPq+tLmetHS9pvCQVFhbaoqKiGI5Wu7JQmVZs\nWaHs9OyYPN6COQvUtnPbo7qPzbs2682Zb+ry0y/XZUWXRWky/4rYiMpD5Tqp4Ume7xAVFxfLL/82\nsS/Wxt9YH39jffyN9fE31icYvNzaWCOpxV63m1cdq0k/UZeLiafmPKVdlbs0pNsQ16PEBHU5AAAA\nHIyXgWiOpFONMa2NMWnaHXre2v8kY0yOpHMlvenhLJC0YccGTSiZoD5n9NGpjU91PU5MhCNhri4H\nAACAWnlWmbPWhowxgyS9JylZ0nPW2gXGmDurvj+u6tQ+kv5jrd3h1SzY7cm5T6o8XK7B3Qe7HiUm\nIjailKQUri4HAACAWnn6HiJr7VRJU/c7Nm6/289Let7LOSCtL12vF0teVN82fXVyw5NdjxMTZaEy\n5abnUpcDAABArWJzeTQ4N3bOWFVGKjW4W2LsDknU5QAAAHBoBKIEsHb7Wr309Uv6VdtfqVVuK9fj\nxEQ4EqYuBwAAgEMiECWAMbPHKGIjurfrva5HiZnycDlXlwMAAMAhEYji3OptqzVp/iT1y++nFjkt\nDv0DcYK6HAAAAOqCQBTnHp/1uIwx+k3X37geJWaoywEAAKCuCERxbMWWFXplwSvq366/mmU3cz1O\nzFCXAwAAQF0RiOLY6FmjlWySNajLINejxBR1OQAAANQVgShOLf95uV5b+Jpu6HCDmtZv6nqcmAlH\nwkpNSqUuBwAAgDohEMWpUTNHKTU5VQM7D3Q9SkyVh8vVIL0BdTkAAADUCYEoDi3bvExTFk/RTR1u\n0nH1jnM9TkxRlwMAAMDhIBDFoZEzRiojJUN3d77b9SgxRV0OAAAAh4tAFGcWb1yst5a8pVsKblHj\nrMaux4kp6nIAAAA4XASiODNyxkjVS6unOwrvcD1KzFGXAwAAwOEiEMWRBRsW6N9L/63bzrxNjTIb\nuR4npqjLAQAA4EgQiOLIyOkj1SC9gW7vdLvrUWKOuhwAAACOBIEoTny9/mu9+927GtBxgHIzcl2P\nE3PU5QAAAHAkCERxYsSMEcpNz9WtHW91PUrMUZcDAADAkSIQxYEv132pD5Z/oDsK71CD9Aaux4m5\n8nC5cjJyqMsBAADgsBGI4sDw6cPVMKOhbjnzFtejOBGOhFUvrZ7rMQAAABBABKKAm7NmjopXFOvu\nznerflrivYeGuhwAAACOBoEo4IbPGK7GmY11U8FNrkdxgrocAAAAjgaBKMBmrJqhz1Z+poFdBior\nNcv1OE5QlwMAAMDRIBAFlLVWw6cP13H1jtON7W90PY4T1OUAAABwtAhEAfX5qs81c81M3dPlHmWm\nZroexwnqcgAAADhaBKIA2rM71LR+U13X7jrX4zhDXQ4AAABHi0AUQJ+u+FRz1s7Rb7r+RhkpGa7H\ncYK6HAAAAKKBQBQw1lo9Ov1RNctupn5t+7kexxnqcgAAAIgGAlHAfPj9h/ryxy91b9d7lZ6SuLsj\n1OUAAAAQDQSiANnz3qGWOS31q7a/cj2OM9TlAAAAEC0EogCZsXmG5v80X4O7DlZqcqrrcZyhLgcA\nAIBoIRAFRMRG9OKKF9Uqt5X65vV1PY5T1OUAAAAQLQSigHhn6TtavmO5hnQbopSkFNfjOENdDgAA\nANFEIAqAiI1oxIwRap7ZXL3P6O16HKeoywEAACCaCEQB8Pa3b2vJpiW6oeUNSk5Kdj2OU9TlAAAA\nEE0EIp8LR8IaOWOkTm98us459hzX4zhFXQ4AAADRRiDyuTcWv6Flm5dpSPchSjaJvTtEXQ4AAADR\nRiDysVAkpJEzR6rNMW10yamXuB7HuXAkrPpp9V2PAQAAgDiSuJcrC4DXF72uH7b8oL9f8XclmcTO\nrnvqcmnJaa5HAQAAQBxJ7FfZPlYZrtSomaPU7rh2+uXJv3Q9jnPU5QAAAOAFdoh86h8L/6GVW1fq\nhd4vEAJEXQ4AAADeYIfIhyrCFRo1c5TObHqmzm99vutxnKMuBwAAAK8QiHxo8jeTtWb7Gt131n3s\nDml3XS43M5e/CwAAAEQdgchnykJlenzW4yo8oVDnnniu63F8IRwJq14qH8YKAACA6CMQ+cyk+ZO0\nrnQdu0NVqMsBAADASwQiH9lVuUtjZo9Rt2bddHaLs12P4wtloTLqcgAAAPAMV5nzkZe+fknrd6zX\n2EvGEgCqRGyEuhwAAAA8ww6RT+ys3Kmxc8aqR4se6t6iu+txfIG6HAAAALzGDpFPvFDygjbu3Khn\nL3/W9Si+URYqU+OsxuyWAQAAwDPsEPlAaUWpxs4Zq6ITi9S5WWfX4/gGdTkAAAB4jUDkAxNKJujn\nsp/1u7N+53oU36AuBwAAgFggEDm2vXy7xs0Zp/Nbn6+Ox3d0PY5vcHU5AAAAxAKByLFnv3xWW8q3\n6L6z7nM9iq9QlwMAAEAsEIgc2lK2RePnjdcvT/6l2jdp73oc36AuBwAAgFghEDn0zLxntK18m4Z0\nH+J6FF+hLgcAAIBYIRA5snnXZj375bO65NRLlH9cvutxfMVaS10OAAAAMUEgcuTpeU9rR8UO/a47\nV5bbWzgSVkpSCnU5AAAAxASByIFNOzfpuS+f0+WnX64zjjnD9Ti+Ql0OAAAAsUQgcuCpuU+pLFTG\n7lANqMsBAAAglghEMbZhxwZNKJmg3mf01imNTnE9jq9QlwMAAECsEYhibOycsaoMV+q33X7rehTf\noS4HAACAWCMQxdCPpT/qpa9eUt+8vjqp4Umux/Ed6nIAAACINQJRDD0x+wlVRio1uOtg16P4DnU5\nAAAAuEAgipE129do4vyJuqbtNTox90TX4/gOdTkAAAC4QCCKkTGzxshaq990/Y3rUXyJuhwAAABc\nIBDFwKqtqzT5m8nql99PLXJauB7Hd6jLAQAAwBUCUQw8PutxGWPYHaoFdTkAAAC4QiDy2A9bftAr\nC17R9e2u1wnZJ7gex5eoywEAAMAVApHHRs8ardSkVA3sMtD1KL60py6XnpLuehQAAAAkIAKRh5b/\nvFyvLXxNN3S4QU3rN3U9ji/tqcsBAAAALhCIPPTYzMeUlpymgZ3ZHapNxEaoywEAAMAZApFHFFgV\nYgAAIABJREFUlm5aqimLpujmgpt1bL1jXY/jS+FIWKlJqdTlAAAA4AyByCMjZ45UZmqm7iq8y/Uo\nvkVdDgAAAK4RiDywaMMivb3kbd1y5i1qnNXY9Ti+RV0OAAAArhGIPDBy5kjVS6unOzrd4XoU36Iu\nBwAAAD8gEEXZNz99o6lLp+r2jrerUWYj1+P4FnU5AAAA+AGBKMpGzhipBukNdHvH212P4mvU5QAA\nAOAHBKIo+nr913rvu/c0oNMA5WTkuB7Ht6jLAQAAwC8IRFE0fPpw5abn6rYzb3M9iq9RlwMAAIBf\nEIiiZN7aefrw+w91Z+c7lZ2e7XocX6MuBwAAAL8gEEXJiBkj1CizkW4uuNn1KL5GXQ4AAAB+QiCK\ngtlrZuuTFZ/o7sK7VT+tvutxfI26HAAAAPyEQBQFw6cP1zFZx+jXBb92PYrvUZcDAACAnxCIjtKs\nNbP0+arPNbDzQGWlZrkex9fCkbBSk6nLAQAAwD9SXA8QVBPnT9TvP/i9Vm1bpSSTpJx0LrN9KGWh\nMh2TdYzrMQAAAIBqBKIjMHH+RA14e4B2Vu6UtLsG9uBHDyo1OVVXtbnK8XT+FbERdtEAAADgK1Tm\njsCDHz5YHYb22BXapUc+e8TRRP5HXQ4AAAB+RCA6Aiu3rqzx+Nrta2M8SXCUhcrUMKOh6zEAAACA\nfRCIjkDLnJY1Hj8h+4QYTxIc1OUAAADgRwSiI/Dw+Q8f8OI+MyVTQ88e6mgif6MuBwAAAL8iEB2B\n/u36a/zl49WiQQsZGTXLbqa/XfA3LqhQC+pyAAAA8CuuMneE+rfrr75t+mrFlhXKTs92PY6vUZcD\nAACAX7FDBE9RlwMAAICfEYjgKepyAAAA8DMCETxFXQ4AAAB+RiCCZ0KREHU5AAAA+BqBCJ4pD5VT\nlwMAAICvEYjgGepyAAAA8DsCETxBXQ4AAABBQCCCJ6jLAQAAIAgIRPAEdTkAAAAEAYEIUUddDgAA\nAEFBIELUUZcDAABAUBCIEHXU5QAAABAUBCJEFXU5AAAABAmBCFFFXQ4AAABBQiBCVFGXAwAAQJAQ\niBA11OUAAAAQNAQiRA11OQAAAAQNgQhRE7ER1Uur53oMAAAAoM4IRIiKPXW5tOQ016MAAAAAdUYg\nQlRQlwMAAEAQEYgQFdTlAAAAEEQEIhw16nIAAAAIKgIRjlpFqIK6HAAAAAKJQISjFrZh6nIAAAAI\nJE8DkTHmImPMEmPMMmPM0FrOKTLGlBhjFhhjPvFyHkQfdTkAAAAEWYpXd2yMSZY0VtIFklZLmmOM\nectau3Cvc3IlPSnpImvtSmPMcV7NA2+Uh8p1TNYxrscAAAAAjoiXO0RdJC2z1i631lZImizpyv3O\nuU7SP621KyXJWvuTh/PAA1xdDgAAAEFmrLXe3LExV2v3zs9tVbdvkNTVWjtor3NGSUqV1FZStqTR\n1toXa7ivAZIGSFKTJk06TZ482ZOZD5eVVUWoQklJsXkrVtmOMmXUy4jJY9XFnn871OV2Ky0tVf36\n9V2PgRqwNv7G+vgb6+NvrI+/sT7u9OrVa561trAu53pWmaujFEmdJJ0vKVPSDGPMTGvtt3ufZK0d\nL2m8JBUWFtqioqJYz1mjslCZVmxZoez07Jg83oI5C9S2c9uYPFZd7KjYoWOyjlHDTK4wJ0nFxcXy\ny79N7Iu18TfWx99YH39jffyN9QkGLwPRGkkt9rrdvOrY3lZL2mSt3SFphzHmU0kdJH0r+B51OQAA\nAASdl12vOZJONca0NsakSeon6a39znlT0tnGmBRjTJakrpIWeTgToiQUCSktOY26HAAAAALNsx0i\na23IGDNI0nuSkiU9Z61dYIy5s+r746y1i4wx70r6WlJE0rPW2m+8mgnRw9XlAAAAEA88fQ+RtXaq\npKn7HRu33+1HJT3q5RyIPupyAAAAiAexuTwa4gp1OQAAAMQLAhEOW3moXLkZua7HAAAAAI4agQiH\nzcpSlwMAAEBcIBDhsIQiIaUmpVKXAwAAQFwgEOGwUJcDAABAPCEQ4bBQlwMAAEA8IRChzqjLAQAA\nIN4QiFBn1OUAAAAQbwhEqDPqcgAAAIg3BCLUCXU5AAAAxCMCEeqEuhwAAADiEYEIdRKxEepyAAAA\niDsEIhxSKBJSWnIadTkAAADEHQIRDom6HAAAAOIVgQiHRF0OAAAA8YpAhIOiLgcAAIB4RiDCQVGX\nAwAAQDwjEOGgqMsBAAAgnhGIUCvqcgAAAIh3BCLUirocAAAA4h2BCLWiLgcAAIB4RyBCjajLAQAA\nIBEQiFAj6nIAAABIBAQi1Ii6HAAAABIBgQgHCEVCSk9Opy4HAACAuEcgwgHKQ+XKychxPQYAAADg\nOQIRDkBdDgAAAImCQIR9UJcDAABAIiEQYR9cXQ4AAACJhECEfURsRFlpWa7HAAAAAGKCQIRq1OUA\nAACQaAhEqFYeKlfDzIauxwAAAABihkCEahEbUWZqpusxAAAAgJghEEESdTkAAAAkJgIRJFGXAwAA\nQGIiEEESdTkAAAAkJgIRqMsBAAAgYRGIQF0OAAAACYtABOpyAAAASFgEogRHXQ4AAACJjECU4KjL\nAQAAIJERiBIcdTkAAAAkMgJRAqMuBwAAgERHIEpg1OUAAACQ6AhECYy6HAAAABIdgShBVYYrqcsB\nAAAg4RGIElRFuIK6HAAAABIegShBUZcDAAAACEQJibocAAAAsBuBKAFRlwMAAAB2IxAlIOpyAAAA\nwG4EogRDXQ4AAAD4XwSiBENdDgAAAPhfBKIEQ10OAAAA+F8EogRCXQ4AAADYF4EogVCXAwAAAPZF\nIEogERtRVmqW6zEAAAAA3yAQJYg9dbnU5FTXowAAAAC+QSBKENTlAAAAgAMRiBIEdTkAAADgQASi\nBEBdDgAAAKgZgSgBUJcDAAAAakYgSgDU5QAAAICaEYjiHHU5AAAAoHYEojhXHiqnLgcAAADUgkAU\n56wsdTkAAACgFgSiOEZdDgAAADg4AlEcoy4HAAAAHByBKI5RlwMAAAAOjkAUp6jLAQAAAIdGIIpT\n1OUAAACAQyMQxSnqcgAAAMChEYjiEHU5AAAAoG4IRHGIuhwAAABQNwSiOERdDgAAAKgbAlGcoS4H\nAAAA1B2BKM5QlwMAAADqjkAUh6jLAQAAAHVDIIojleFKpSWnUZcDAAAA6ohAFEeoywEAAACHh0AU\nZ6jLAQAAAHVHIIoT1OUAAACAw0cgihPU5QAAAIDDRyCKE3wYKwAAAHD4CERxoDJcqYyUDOpyAAAA\nwGEiEMWB8lC5cjNyXY8BAAAABA6BKA5QlwMAAACODIEo4KjLAQAAAEeOQBRw1OUAAACAI0cgCjjq\ncgAAAMCRIxAFGHU5AAAA4OgQiAKMuhwAAABwdAhEAUZdDgAAADg6BKKAoi4HAAAAHD0CUUBRlwMA\nAACOHoEooKjLAQAAAEePQBRAleFKZaZmUpcDAAAAjhKBKIDKQ+XKSc9xPQYAAAAQeASiAKIuBwAA\nAEQHgShgqMsBAAAA0UMgChjqcgAAAED0EIgChrocAAAAED0EogCx1lKXAwAAAKKIQBQg1lrlpvNh\nrAAAAEC0EIgCJjM10/UIAAAAQNwgEAVEZbhSxhjqcgAAAEAUEYgCojxUrpSkFNdjAAAAAHGFQBQQ\nVlbGGNdjAAAAAHGFQBQAez6M1YhABAAAAEQTgSgAykPlXF0OAAAA8ACBKACsLFeXAwAAADxAIPK5\ninAFH8YKAAAAeIRA5HMVoQrqcgAAAIBHCEQ+R10OAAAA8A6ByMeoywEAAADe8jQQGWMuMsYsMcYs\nM8YMreH7RcaYrcaYkqo/D3k5T9BQlwMAAAC8leLVHRtjkiWNlXSBpNWS5hhj3rLWLtzv1GnW2su8\nmiPIqMsBAAAA3vJyh6iLpGXW2uXW2gpJkyVd6eHjxRXqcgAAAID3vAxEzSSt2uv26qpj+zvLGPO1\nMeYdY0xbD+cJFOpyAAAAgPeMtdabOzbmakkXWWtvq7p9g6Su1tpBe53TQFLEWltqjLlE0mhr7ak1\n3NcASQMkqUmTJp0mT57sycyHy8qqIlShpKTo58pIJKK0lDQZmepjpaWlql+/ftQfC9HB+vgXa+Nv\nrI+/sT7+xvr4G+vjTq9eveZZawvrcq5n7yGStEZSi71uN686Vs1au22vr6caY540xhxjrd2433nj\nJY2XpMLCQltUVOTZ0IejLFSmFVtWKDs9O6r3WxGuUJJJUsuclvscLy4ull9+dxyI9fEv1sbfWB9/\nY338jfXxN9YnGLyszM2RdKoxprUxJk1SP0lv7X2CMaapMcZUfd2lap5NHs4UCNTlAAAAgNjwbIfI\nWhsyxgyS9J6kZEnPWWsXGGPurPr+OElXS7rLGBOStEtSP+tVhy9ArKyy0rJcjwEAAADEPS8rc7LW\nTpU0db9j4/b6+glJT3g5Q9DsubpcSpKnSwMAAABAHn8wKw4fdTkAAAAgdghEPkNdDgAAAIgdApGP\nUJcDAAAAYotA5CPU5QAAAIDYIhD5CHU5AAAAILYIRD5BXQ4AAACIPQKRT1CXAwAAAGKPQOQT1OUA\nAACA2CMQ+QB1OQAAAMANApEPUJcDAAAA3CAQ+QB1OQAAAMANApFj1OUAAAAAdwhEjpWHyqnLAQAA\nAI4QiHyAuhwAAADgBoHIIepyAAAAgFsEIoeoywEAAABuEYgcoy4HAAAAuEMgcoS6HAAAAOAegciR\nijAfxgoAAAC4RiByxFo+jBUAAABwjUDkAHU5AAAAwB8IRA5QlwMAAAD8gUDkAHU5AAAAwB8IRDFG\nXQ4AAADwDwJRjFGXAwAAAPyDQOQAdTkAAADAHwhEMVQRrlBGSgZ1OQAAAMAnCEQxRF0OAAAA8BcC\nUYxRlwMAAAD8g0AUI9TlAAAAAP8hEMUIdTkAAADAfwhEMURdDgAAAPAXAlEMVIQrlJnCh7ECAAAA\nfkMgioGKcIVy0nNcjwEAAABgPwSiGKEuBwAAAPgPgchj1OUAAAAA/yIQeYy6HAAAAOBfBKIYoC4H\nAAAA+BOByEPU5QAAAAB/IxB5iLocAAAA4G8EIo9RlwMAAAD8i0DkEepyAAAAgP8RiDxCXQ4AAADw\nPwKRh6jLAQAAAP5GIPJARbhCWSlZ1OUAAAAAnyMQeaAiXKEG6Q1cjwEAAADgEAhEHrDWUpcDAAAA\nAoBAFGUV4QrVS61HXQ4AAAAIAAJRlFGXAwAAAIKDQBRl1OUAAACA4CAQRRF1OQAAACBYCERRVBGu\nUE4GH8YKAAAABAWBKIqstcpMzXQ9BgAAAIA6IhBFCXU5AAAAIHgIRFFCXQ4AAAAIHgJRlFCXAwAA\nAIKHQBQF1OUAAACAYCIQRUFFuEK5mbmuxwAAAABwmAhEUWCtVUZKhusxAAAAABymuOh4VVZWavXq\n1SorK4vp41prVRmpVJJJ0tINSz1/vJycHC1atMjzx0lUGRkZat68uVJTU12PAgAAgBiJi0C0evVq\nZWdnq1WrVjLGxOxxIzai8lC50pLTlJyU7Pnjbd++XdnZ2Z4/TiKy1mrTpk1avXq1Wrdu7XocAAAA\nxEhcVObKysrUuHHjmIahPYwxSjJx8deY0Iwxaty4ccx3GQEAAOBW3LySdxGGJCnZJDt7bEQX6wgA\nAJB44iYQuZBkkpSSlKJNmzapoKBABQUFatq0qZo1a1Z9u6Kiok73dfPNN2vJkiUHPWf8+PGaOHFi\nNEYHAAAAoDh5D9FhmzhRevBBaeVKqWVL6eGHpf79j+iu9lStSkpKJEl/+tOfVL9+fd133337nGet\nlbVWSUk1Z9AJEyYc8rEGDBjgy/cQHep3AwAAAPwq8V7BTpwoDRggrVghWbv7fwcM2H08ypYtW6a8\nvDz1799fbdu21bp16zRgwAAVFhaqbdu2GjZsWPW5Z599tkpKShQKhZSbm6uhQ4eqQ4cO6t69u376\n6SdJ0rBhwzRq1Kjq84cOHaouXbro9NNP1/Tp0yVJO3bsUN++fZWXl6err75ahYWF1WFtb/fff7/y\n8vLUvn17PfDAA5KkH3/8UVdeeaXat2+vDh06aNasWZKkv/3tb8rPz1d+fr7GjBlT6+/2zjvvqHv3\n7urYsaOuueYa7dixI+p/pwAAAEA0xd8O0eDBUg0BoNrMmVJ5+b7Hdu6Ubr1VeuaZmn+moECqCiKH\na/HixXrxxRdVWFgoSXrkkUfUqFEjhUIh9erVS1dffbXy8vL2+ZmtW7fq3HPP1SOPPKIhQ4boueee\n09ChQw+4b2utZs+erbfeekvDhg3Tu+++qzFjxqhp06Z6/fXX9dVXX6ljx44H/Nz69es1depULViw\nQMYYbdmyRZI0cOBAXXDBBRo0aJBCoZB27typWbNmaeLEiZozZ45CoZC6dOmioqIiZWZm7vO7/fTT\nT3rkkUf04YcfKisrSw8//LBGjx6tP/zhD0f09wYAAADEQuLtEO0fhg51/CidfPLJ1WFIkiZNmqSO\nHTuqY8eOWrRokRYuXHjAz2RmZuriiy+WJHXq1Ek//PBDjfd91VVXHXDOZ599pn79+kmSOnTooLZt\n2x7wc40aNVJSUpJuv/12TZkyRfXq1ZMkFRcX64477pAkpaSkqEGDBvrss8/Ut29fZWZmKjs7W717\n99a0adMO+N2mT5+uhQsX6qyzzlJBQYEmTpxY69wAAACAX8TfDtGhdnJatdpdk9vfiSdKxcVRH2dP\n2JCkpUuXavTo0Zo9e7Zyc3N1/fXX13iZ57S0tOqvk5OTFQqFarzv9PT0Q55Tk9TUVM2dO1fvv/++\n/vGPf+ipp57Sf/7zH0mHd6W1vX83a60uuugivfTSS3X+eQAAAMC1xNshevhhKStr32NZWbuPe2zb\ntm3Kzs5WgwYNtG7dOr333ntRf4wePXro1VdflSTNnz+/xh2o7du3a9u2bbrsssv02GOP6csvv5Qk\n9erVS+PGjZMkhcNhbdu2TT179tSUKVO0a9culZaW6s0331TPnj0PuM+zzjpLn3zyiZYvXy5p93uZ\nli5dGvXfDwAAAIim+NshOpQ9V5OL0lXmDkfHjh2Vl5enM844QyeeeKJ69OgR9ce45557dOONNyov\nL6/6T05Ozj7nbN26VVdddZXKy8sViUQ0cuRISdITTzyh22+/XU8//bRSUlL09NNPq0uXLrr22mvV\nuXNnSdJdd92ldu3aadmyZfvcZ5MmTfT3v/9d11xzTfWlxv/yl7/o1FNPjfrvCAAAAESLsda6nuGw\nFBYW2rlz5+5zbNGiRWrTpo2jiWJn+/bth7zsdigUUigUUkZGhpYuXaoLL7xQS5cuVUpK4mXfI3E0\n/5aKi4tVVFQU3YEQFayNv7E+/sb6+Bvr42+sjzvGmHnW2sJDn5mIO0RxrrS0VOeff75CoZCstdW7\nPQAAAAAOxCvlOJObm6t58+a5HgMAAAAIhMS7qAIAAAAAVCEQAQAAAEhYBCIAAAAACYtABAAAACBh\nEYii5Mcff1S/fv108sknq1OnTrrkkkv07bffuh6rRq1atdLGjRsl7f5A1ZrcdNNNeu211w56P88/\n/7zWrl1bffu2226r8YNgAQAAAL9KyEA0cf5EtRrVSkl/TlKrUa00cf7Eo7o/a6369OmjoqIifffd\nd5o3b57++7//W+vXr9/nvFAodFSP44Xp06cf8c/uH4ieffZZ5eXlRWOsqPLj3zsAAAD8IeEC0cT5\nEzXg7QFasXWFrKxWbF2hAW8POKpQ9PHHHys1NVV33nln9bEOHTqoZ8+eKi4uVs+ePXXFFVdUh4WR\nI0cqPz9f+fn5GjVqlCRpx44duvTSS9WhQwfl5+frlVdekSQNHTpUeXl5at++vR588MEDHnvcuHG6\n//77q28///zzGjRokCSpd+/e6tSpk9q2bavx48fXOHv9+vUl7Q51gwYN0umnn65f/OIX+umnn6rP\nGTZsmDp37qz8/HwNGDBA1lq99tprmjt3rvr376+CggLt2rVLRUVF2vOhuZMmTVK7du2Un5+vBx54\nYJ/He/DBB9WhQwd169btgNAoSZ988okKCgpUUFCgM888U9u3b5ck/fWvf1W7du3UoUMHDR06VJJU\nUlKibt26qX379urTp49+/vlnSVJRUZEGDx6swsJCjR49Whs2bFDfvn3VuXNnde7cWZ9//nntCwoA\nAICEEXefQzT43cEq+bGk1u/PXD1T5eHyfY7trNypW9+8Vc/Me6bGnyloWqBRF42q9T6/+eYbderU\nqdbvf/HFF/rmm2/UunVrzZs3TxMmTNCsWbNkrVXXrl117rnnavny5TrhhBP073//W5K0detWbdq0\nSVOmTNHixYtljNGqVasOuO++ffuqe/fuevTRRyVJr7zySnVweu6559SoUSPt2rVLnTt3Vt++fdW4\nceMaZ5wyZYqWLFmihQsXav369crLy9Mtt9wiSRo0aJAeeughSdINN9ygf/3rX7r66qv1xBNPaPjw\n4Sos3PdDgNeuXasHHnhA8+bNU8OGDXXhhRfqjTfeUO/evbVjxw5169ZNDz/8sP7rv/5LzzzzjP74\nxz/u8/PDhw/X2LFj1aNHD5WWliojI0PvvPOO3nzzTc2aNUtZWVnavHmzJOnGG2/UmDFjdO655+qh\nhx7Sn//85+qQWVFRUR3QrrvuOv32t7/V2WefrZUrV+qXv/ylFi1aVOuaAQAAIDEk3A7R/mHoUMej\noUuXLmrdurUk6bPPPlOfPn1Ur1491a9fX1dddZWmTZumdu3a6f3339cDDzygadOmKScnRzk5OcrI\nyNCtt96qf/7zn8rKyjrgvo899liddNJJmjlzpjZt2qTFixerR48ekqTHH3+8eidm1apVWrp0aa0z\nfvrpp7r22muVnJysE044Qeedd1719z7++GN17dpV7dq100cffaQFCxYc9PedM2eOioqKdOyxxyol\nJUX9+/fXp59+KklKS0vTZZddJknq1KmTfvjhhwN+vkePHhoyZIgef/xxbdmyRSkpKfrggw908803\nV/8dNGrUSFu3btWWLVt07rnnSpJ+/etfVz+OJF1zzTXVX3/wwQcaNGiQCgoKdMUVV2jbtm0qLS09\n6O8BAACA+Bd3O0QH28mRpFajWmnF1hUHHD8x50QV31R8RI/Ztm3bg16AoF69eoe8j9NOO01ffPGF\npk6dqj/+8Y86//zz9dBDD2n27Nn68MMP9dprr2n06NH66KOPqnejrrjiCg0bNkz9+vXTq6++qjPO\nOEN9+vSRMUbFxcX64IMPNGPGDGVlZamoqEhlZWWH/buVlZXp7rvv1ty5c9WiRQv96U9/OqL72SM1\nNVXGGElScnJyje/vGTp0qC699FJNnTpVPXr00HvvvXdEj7X333skEtHMmTOVkZFxZIMDAAAgLiXc\nDtHD5z+srNR9d1qyUrP08PkPH/F9nnfeeSovL9/nfTpff/21pk2bdsC5PXv21BtvvKGdO3dqx44d\nmjJlinr27Km1a9cqKytL119/ve6//3598cUXKi0t1datW3XJJZfoscce0/z585WcnKySkhKVlJRo\n2LBhkqQ+ffrozTff1KRJk9SvXz9Juyt3DRs2VFZWlhYvXqyZM2ce9Hc455xz9MorrygcDmvdunX6\n+OOPJak6/BxzzDEqLS3dJ/hlZ2dXv79nb126dNEnn3yijRs3KhwOa9KkSdW7OHXx3XffqV27dnrg\ngQfUuXNnLV68WBdccIEmTJignTt3SpI2b96snJwcNWzYsPrv+aWXXqr1cS688EKNGTOm+nZJSe21\nSgAAACSOuNshOpT+7fpLkh788EGt3LpSLXNa6uHzH64+fiSMMZoyZYoGDx6sv/71r8rIyFCrVq00\natQorVmzZp9zO3bsqJtuukldunSRtPtS1Weeeabee+893X///UpKSlJqaqqeeuopbd++XVdeeaXK\nyspkrdVf/vKXGh+/YcOGatOmjRYuXFh9vxdddJHGjRunNm3a6PTTT1e3bt0O+jv06dNHH330kfLy\n8tSyZUt1795dkpSbm6vbb79d+fn5atq0qTp37lz9MzfddJPuvPNOZWZmasaMGdXHjz/+eD3yyCPq\n1auXrLW69NJLdeWVV9b573PUqFH6+OOPlZSUpLZt2+riiy9Wenq6SkpKVFhYqLS0NF1yySX6y1/+\nohdeeEF33nmndu7cqZNOOkkTJkyo8T4ff/xxDRw4UO3bt1coFNI555yjcePG1XkmAAAAxCdjrXU9\nw2EpLCy0e94ov8eiRYvUpk0bRxPFzvbt25Wdne16jLh2NP+WiouLVVRUFN2BEBWsjb+xPv7G+vgb\n6+NvrI87xph51trCQ5+ZgJU5AAAAANiDQAQAAAAgYRGIAAAAACSsuAlEQXsvFPyHf0MAAACJJy4C\nUUZGhjZt2sQLWhwxa602bdrE5xQBAAAkmLi47Hbz5s21evVqbdiwwfUoniorK+MFu4cyMjLUvHlz\n12MAAAAghuIiEKWmpqp169aux/BccXGxzjzzTNdjAAAAAHEjLipzAAAAAHAkCEQAAAAAEhaBCAAA\nAEDCMkG7MpsxZoOkFa7ncOQYSRtdD4FasT7+xdr4G+vjb6yPv7E+/sb6uHOitfbYupwYuECUyIwx\nc621ha7nQM1YH/9ibfyN9fE31sffWB9/Y32CgcocAAAAgIRFIAIAAACQsAhEwTLe9QA4KNbHv1gb\nf2N9/I318TfWx99YnwDgPUQAAAAAEhY7RAAAAAASFoHIp4wxPxhj5htjSowxc6uONTLGvG+MWVr1\nvw1dz5kojDHPGWN+MsZ8s9exWtfDGPN7Y8wyY8wSY8wv3UydOGpZnz8ZY9ZUPYdKjDGX7PU91idG\njDEtjDEfG2MWGmMWGGPurTrO88cHDrI+PH98wBiTYYyZbYz5qmp9/lx1nOePDxxkfXj+BAyVOZ8y\nxvwgqdBau3GvY3+TtNla+4gxZqikhtbaB1zNmEiMMedIKpX0orU2v+pYjethjMmTNEkMbXWJAAAH\nJ0lEQVRSF0knSPpA0mnW2rCj8eNeLevzJ0ml1trh+53L+sSQMeZ4Scdba78wxmRLmiept6SbxPPH\nuYOsz6/E88c5Y4yRVM9aW2qMSZX0maR7JV0lnj/OHWR9LhLPn0BhhyhYrpT0QtXXL2j3/9FCDFhr\nP5W0eb/Dta3HlZImW2vLrbXfS1qm3f/xg0dqWZ/asD4xZK1dZ639ourr7ZIWSWomnj++cJD1qQ3r\nE0N2t9Kqm6lVf6x4/vjCQdanNqyPTxGI/MtK+sAYM88YM6DqWBNr7bqqr3+U1MTNaKhS23o0k7Rq\nr/NW6+AvMOCde4wxX1dV6vZUSlgfR4wxrSSdKWmWeP74zn7rI/H88QVjTLIxpkTST5Let9by/PGR\nWtZH4vkTKAQi/zrbWlsg6WJJA6sqQdXs7q4jfUefYD186SlJJ0kqkLRO0gi34yQ2Y0x9Sa9LGmyt\n3bb393j+uFfD+vD88Qlrbbjq9UBzSV2MMfn7fZ/nj0P/v737D7mzrOM4/v7kk2aPLaFyjBQyHLUt\n02zbH2a4kK0f/4n9MhHRQRYWlhCIf1TioEVl2Q+oiYQ1S4duOQQlihCWy6ljP9wzI0SFnnIWsX6x\nWbZvf5zr2OHsOTnH9pzzeN4veDj3fZ/rvu/vfS4u7vN9ruu+zoD6sf3MMSZEI6qqptvrc8AmOl2q\n+9p47+647+eGF6EYXB/TwBk95U5v2zSLqmpfu1EdAm7lf8MSrJ9Z1sbW3wPcUVUb22bbz4iYqX5s\nP6OnqvYDv6LzfIrtZ8T01o/tZ+4xIRpBSSbbw60kmQRWAY8Dm4ErWrErgHuHE6GaQfWxGfh4kpOS\nnAksBLYNIb6x1v2y0FxMpw2B9TOr2kPHtwF7q+rmnrdsPyNgUP3YfkZDkjclObUtnwysBJ7A9jMS\nBtWP7WfumRh2AJrRfGBT5z7FBPCTqnogySPAhiSrgWfozAKkWZDkp8AK4I1Jfg98CVjLDPVRVXuS\nbACmgBeAa5xB5vgaUD8rkpxLZyjJ08DVYP0MwXuAy4HdbZw9wA3YfkbFoPq51PYzEhYAtyc5gc4/\nsTdU1X1JtmL7GQWD6ufHtp+5xWm3JUmSJI0th8xJkiRJGlsmRJIkSZLGlgmRJEmSpLFlQiRJkiRp\nbJkQSZIkSRpbJkSSJACSvCHJjvb3bJLpnvUTj/AYP0zytpcoc02Sy45N1KMhyZY2za4kaY5x2m1J\n0mGSfBn4R1V9vW976Nw7Dg0lsBGVZAvwmara8ZKFJUkjxR4iSdL/leSsJFNJ7gD2AAuSrEvyaJI9\nSb7YU3ZLknOTTCTZn2Rtkp1JtiY5rZVZk+RzPeXXJtmW5LdJzm/bJ5Pc0857dzvXYT0wSZYleTDJ\nY0nuTzI/yavb+gWtzNeS3NiWb0zySJLHk3y/JXjdOG5u55lKsjTJpiS/a8lh93PYk+TOJHuTbGi/\nTt8f0wfb9W5PcleSyZ44ppLsSvLVY1pJkqSjZkIkSToSbwe+WVWLq2oauL6qlgLnACuTLJ5hn9cD\nD1bVOcBW4KoBx05VLQe+AHSTq88Cz1bVYuAm4F2H7ZScBNwCXFJV7wbWAzdV1b+BK4F1SVYB7wPW\ntN1uqaplwNktvg/0HPJAu6bbgJ8Bn2rlPpnk1FZmMfCtqloEHKT9An1PTKcB1wMXVdV5wC7g2iTz\ngQ8BS6rqncBXBnwWkqRZZkIkSToST1bVoz3rlybZDmwHFtFJFPodqKr72/JjwFsGHHvjDGUuAO4E\nqKqddHqm+i0ClgC/SLKDTiJyRttnV9v/XuCqliQBXJRkG7ATuLDt37W5ve4GdlfVvqo6CDwNnN7e\ne6qqftOW17c4e51P57N4qMV0WbumvwCHgFuTXAz8c8BnIUmaZRPDDkCSNCe8+AU+yULgWmB5Ve1P\nsh54zQz7/Ktn+T8Mvuc8fwRlZhJgV1W9d8D77wD+CnSH6r0W+C5wXlVNJ1nTF3c3jkM9y931blz9\nD972rwd4oKouPyzYZCmwEvgI8Glg1eBLkyTNFnuIJEkv1zzg78DfkiwA3n8czvFr4KMASc5m5h6o\nKeDNSZa3cicmWdKWPwacAqwAvpdkHnAyneTmz0leB1xyFHGdmWRZW/4EsKXv/YeAC5O8tcUxmWRh\nO9+8qroP+DwzDAGUJA2HPUSSpJdrO51k5AngGTrJy7H2HeBHSabauabo9Pa8qKqeT/Jh4Nst4TkB\n+EaSP9F57mhFVf0hyQ/oPP+0Osnt7Vh/BB4+irj2Ate1CR52A+v6YtqXZDVwV89U5TcAB4CN7bmn\nVwHXHcW5JUnHgdNuS5JGTpIJYKKqDrYhej8HFlbVC0OM6Szg7qry94Yk6RXEHiJJ0ig6BfhlS4wC\nXD3MZEiS9MplD5EkSZKkseWkCpIkSZLGlgmRJEmSpLFlQiRJkiRpbJkQSZIkSRpbJkSSJEmSxpYJ\nkSRJkqSx9V+MsLixdlgztQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b3b74245f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot!\n",
    "skplt.estimators.plot_learning_curve(rf, X, y)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
